Currently, fingerprint recognition has a broad application, such as applications in a criminal investigation or in fingerprint password recognitions. In an actual fingerprint recognition application, a latent fingerprint may need to be compared. For example, in the criminal investigation, a possible suspect may be identified effectively by recognizing the fingerprint extracted in the criminal field. The latent fingerprint refers to a fingerprint left in the criminal field, i.e., a fingerprint trace left by contacting an object.
In an actual application, the fingerprint recognition method is mainly based on a comparison of minutiae, that is to say, the minutiae are extracted to represent a fingerprint image. The comparison typically comprises two main procedures, which are processing procedure and comparing procedure. The processing procedure may, for example, include a valid area estimation, an orientation field estimation, a ridge extraction, a ridge thinning and a minutiae extraction, in which the orientation field estimation is a key step in the method of minutiae comparison. The comparing procedure may for example include minutiae match and minutiae comparison.
However, since most of the fingerprints in the field are distorted or overlapped fingerprints, a quality of the extracted fingerprints may be undesirable. Although traditional fingerprint processing algorithms have a good performance in processing fingerprints extracted professionally, they are comparatively incapable in processing the latent fingerprints extracted.